<h1 id="florida-congressional-districts">2020 Florida Congressional
Districts</h1>
<h2 id="redistricting-requirements">Redistricting requirements</h2>
<p>In Florida, according to <a
href="http://www.leg.state.fl.us/statutes/index.cfm?submenu=3#A3S20">the
state constitution Art. III §§ 20</a>, districts must: 1. not be drawn
with the intent to favor or disfavor a political party or an incumbent
2. not be drawn with the intent or result of denying or abridging the
electoral opportunities of racial or language minorities (The following
are required in so much as they do not impose on the above requirements)
3. be as nearly equal in population as is practicable 4. be compact 5.
utilize existing political and geographical boundaries 6. preserve
county and municipality boundaries as much as possible</p>
<h3 id="algorithmic-constraints">Algorithmic Constraints</h3>
<p>We enforce a maximum population deviation of 0.5%.</p>
<h2 id="data-sources">Data Sources</h2>
<p>Data for Florida comes from the ALARM Project’s <a
href="https://alarm-redist.github.io/posts/2021-08-10-census-2020/">2020
Redistricting Data Files</a>. Data for Florida’s 2020 congressional
district map comes from the <a
href="https://davesredistricting.org/maps#home">Dave’s
Redistricting</a></p>
<h2 id="pre-processing-notes">Pre-processing Notes</h2>
<p>We estimate CVAP populations with the <a
href="https://github.com/christopherkenny/cvap">cvap</a> R package. We
also pre-process the map to split it into clusters for simulation, which
has a slight effect on the types of redistrict plans that will be
sampled.</p>
<h2 id="simulation-notes">Simulation Notes</h2>
<p>We sample 160,000 districting plans for Florida across two
independent runs of the SMC algorithm, and then thin the sample down to
5,000 plans. Due to the size, shape, and complexity of Florida, we split
the simulations into multiple steps.</p>
<ol type="1">
<li><p><strong>Regional clustering</strong>. First, we cluster Florida
counties into 3 regions–Southern Florida, Northern Florida, and Central
Florida–with the following county assignments:</p>
<p>Southern Florida: Broward, Charlotte, Collier, DeSoto, Glades,
Hardee, Hendry, Highlands, Lee, Manatee, Martin, Miami-Dade, Monroe,
Okeechobee, Palm Beach, Sarasota, and St. Lucie</p>
<p>Northern Florida: Alachua, Baker, Bay, Bradford, Calhoun, Clay,
Columbia, Dixie, Duval, Escambia, Franklin, Gadsden, Gilchrist, Gulf,
Hamilton, Holmes, Jackson, Jefferson, Lafayette, Leon, Levy, Liberty,
Madison, Marion, Nassau, Okaloosa, Putnam, Santa Rosa, St. Johns,
Suwannee, Taylor, Union, Wakulla, Walton, and Washington</p>
<p>Central Florida: Brevard, Citrus, Flagler, Hernando, Hillsborough,
Indian River, Lake, Lake, Orange, Osceola, Pasco, Pinellas, Polk,
Seminole, Sumter, and Volusia</p>
<p>County assignments were based on the collections of counties that
define Metropolitan and Combined Statistical Areas and on past and
current Congressional district maps.</p></li>
<li><p><strong>Simulating Northern and Southern Florida</strong>. We run
simulations first in Northern and Southern Florida. These simulations
run the SMC algorithm within each cluster with a 0.5% population
tolerance. Because each cluster will have leftover population, we apply
an additional constraint that encourages unassigned areas to be set on
each cluster’s border with the Central Florida cluster, thereby avoiding
district discontiguities.</p>
<p>In both the Northern Florida cluster and the Southern Florida
cluster, we apply Gibbs constraints to encourage the formation of Black
and Hispanic opportunity districts. To balance county and municipality
splits, we create pseudocounties for use in the county constraint, which
leads to fewer municipality splits than using only a county
constraint.</p></li>
<li><p><strong>Simulating Central Florida</strong>. The partial map
simulations from the Southern and Northern Florida clusters are then
combined, with unassigned areas being absorbed into the Central Florida
cluster. We then run simulations in Central Florida, applying Gibbs
hinge constraints to encourage the formation of minority opportunity
districts. To limit county and municipality splits, we create
pseudocounties for use in the county constraint.</p></li>
</ol>
<h2 id="contents">Contents</h2>
<ul>
<li><code>FL_cd_2020_stats.csv</code> contains summary statistics on the
sampled redistricting plans</li>
<li><code>FL_cd_2020_plans.rds</code> is a compressed
<code>redist_plans</code> object, which contains the matrix of
precinct/block assignments and may be used for further analysis.</li>
<li><code>FL_cd_2020_map.rds</code> is a compressed
<code>redist_map</code> object, which contains the precinct/block
shapefile and demographic data.</li>
</ul>
<p>Both the <code>redist_plans</code> and <code>redist_map</code> object
are intended to be used with the <a
href="https://alarm-redist.github.io/redist/">redist package</a>.</p>
<h3 id="codebook-for-summary-statistics">Codebook for summary
statistics</h3>
<ul>
<li><code>draw</code>: unique identifier for each sample. Non-numeric
draw names are real-world plans, e.g., <code>cd_2010</code> for an
enacted 2010 plan.</li>
<li><code>district</code>: a district identifier. District numbers
roughly match those in the enacted plan, but the correspondence is not
perfect.</li>
<li><code>chain</code>: a number identifying the run of the
redistricting algorithm used to produce this draw. Used for diagnostic
purposes.</li>
<li><code>pop_overlap</code>: a number indicating the fraction of people
in this plan who reside in the same-numbered district in the enacted
plan.</li>
<li><code>total_pop</code>: the total population of each district.</li>
<li><code>total_vap</code>: the total voting-aged population of each
district.</li>
<li><code>pop_*</code>, <code>vap_*</code>: total (voting-aged)
population within racial and ethnic groups for each district. Variable
codes documented <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>plan_dev</code>: the maximum population deviation among
districts in the plan. Computed as
<code>max(abs(distr_pop - target_pop)/target_pop)</code>.</li>
<li><code>comp_edge</code>: compactness, as measured by the fraction of
internal edges kept. Higher values indicate more compactness.</li>
<li><code>comp_polsby</code>: compactness, as measured by the
Polsby-Popper score. Higher values indicate more compactness.</li>
<li><code>county_splits</code>: the number of counties which belong to
more than one district.</li>
<li><code>muni_splits</code>: the number of Census Designated Places
which belong to more than one district.</li>
<li><code>*_##_dem_*</code>, <code>*_##_rep_*</code>: vote counts for
statewide Democratic and Republican candidates in a certain election.
More information <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>adv_##</code>, <code>arv_##</code>: average vote counts for
statewide Democratic and Republican candidates in a certain year. More
information <a
href="https://github.com/alarm-redist/census-2020#data-format">here</a>.</li>
<li><code>ndv</code>, <code>nrv</code>: averages of the
<code>adv_##</code> and <code>arv_##</code> variables across all
available elections.</li>
<li><code>ndshare</code>: normal Democratic share, computed as
<code>ndv / (ndv + nrv)</code></li>
<li><code>e_dvs</code>: average Democratic vote share, computed as the
average of the Democratic vote share when first scored under each
statewide election.</li>
<li><code>pr_dem</code>: probability seat is represented by a Democrat;
calculated as the fraction of statewide elections under which the
district had a majority Democratic share.</li>
<li><code>e_dem</code>: expected number of Democratic seats for the
plan; equivalent to summing the <code>pr_dem</code> values across
districts</li>
<li><code>pbias</code>: partisan bias at 50% vote share, averaged across
all available elections. Positive values indicate Republican bias.</li>
<li><code>egap</code>: the efficiency gap, averaged across all available
elections. Positive values indicate Republican bias.</li>
</ul>
